Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment.

翻译：在拒止环境下，由于无法依赖全球导航卫星系统（GNSS）、地图构建、数据共享及中央处理等辅助手段，集群机器人执行搜索任务极具挑战性。然而，通过模拟生物利用嗅觉与听觉信号协同工作的方式，有望成为提升集群机器人协作能力的重要途径。本文提出了一种面向自主机器人集群的嗅觉-听觉增强型Bug算法（OA-Bug），用于探索拒止环境。为评估OA-Bug的性能，我们构建了仿真环境。实验表明，采用OA-Bug的搜索任务覆盖率可达96.93%，相较于同类算法SGBA有显著提升。此外，通过真实集群机器人实验验证了OA-Bug的有效性，结果表明该算法能有效提升拒止环境下集群机器人的任务执行性能。